• Single Headed Attention RNN: Stop Thinking With Your Head
  The leading approaches in language modeling are all obsessed with TV shows of my youth - namely Transformers and Sesame Street. Transformers this, Transformers that, and over here a bonfire worth of GPU-TPU-neuromorphic wafer scale silicon. We opt for the lazy path of old and proven techniques with a fancy crypto inspired acronym: the Single Headed Attention RNN (SHA-RNN). The author's lone goal is to show that the entire field might have evolved a different direction if we had instead been obsessed with a slightly different acronym and slightly different result...

• Diversity in AI is not your problem, it’s hers
  I came to a shocking conclusion while writing about diversity for my book on machine learning: diversity in Artificial Intelligence is not your problem, it’s hers. I mean, of course, that the problem is with the English pronoun “hers”...

• Amazon's Ring Planned Neighborhood "Watch Lists" built on Facial Recognition
  Ring, Amazon's crimefighting surveillance camera division, has crafted plans to use facial recognition software and its ever-expanding network of home security cameras to create AI-enabled neighborhood “watch lists,” according to internal documents reviewed by The Intercept....

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• Alexa’s voice can now express disappointment and excitement
  You might hear a little more emotion from Alexa’s vaguely-robotic voice some time soon, as Amazon announced today that developers can now have Alexa respond to questions from US users with a “happy/excited” or a “disappointed/empathetic” tone...

• Data Science is Boring (Part 1)
  Many people cherrypick the exciting parts of doing Data Science (or ML, Machine Learning) to motivate themselves and others. But we must face a reality: the real work is often “boring” — boring as comparing to what people romanticize. Feeling bored creates tension; it, ultimately, leads to a high turnover rate in data science. I want to share what I actually do and how I cope with the “boringness in data science”. I hope to help you, the aspiring Data Scientists, to set the right expectations. So, once you decide to pursue a Data Science career, you are in it for the long game. Enjoy...

• We tried teaching an AI to write Christmas movie plots. Hilarity ensued. Eventually.
  Using a neural network to create ridiculous plot lines takes a lot of work - and reveals the challenges of generating human language...

• What does a Fine-tuned BERT model look at?
  An attempt to understand features and patterns learnt by a Fine-tuned BERT model...

• Optimizing agent behavior over long time scales by transporting value
  Humans perform “mental time travel” across memories for goal-directed decisions. Our new algorithm, also based on episodic memory retrieval, enables AI agents to perform long-term credit assignment...

• AI will disrupt white-collar workers the most, predicts a new report
  Researchers looked at the text of AI patent and job descriptions, and quantified the overlap in order to identify the kinds of tasks and occupations likely to be affected. The analysis says that AI will be a significant factor in the future work lives of managers, supervisors, and analysts, shaking up all sorts of white-collar work from law firms, marketing roles, and publishers to computer programming...

• MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
  We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation. We build upon FineGAN, an unconditional generative model, to learn the desired disentanglement and image generator, and leverage adversarial joint image-code distribution matching to learn the latent factor encoders...

• AI for Healthcare
  What happens when you bring physicians, surgeons, CEOs, CTOs, COOs, strategic directors, product managers, software engineers, data analysts, professors and researchers together to discuss the current state of artificial intelligence in healthcare? Well, that’s the idea behind Ai4 Healthcare. This is my experience at the conference...

• The Three Kinds Of Data Science Project Exams That Show Up In A Data Science Interview
  You've got an interview and you've found out an exam will be given to you. All that you've been told is that, at some point of your choosing in the next few weeks, you'll be given 4 hours to ingest and operate on a sizable data set using your programming language of choice...

 
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• Data Scientist - Driven Brands - Charlotte, NC
  
  The Data Scientist for Driven Brands focus will be responsible for providing reliable marketing, media and promotional performance analysis and reporting to Senior Executives and Business Unit Management to be used to make decisions impacting the performance of the business...

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• RoboNet: A Dataset for Large-Scale Multi-Robot Learning
  Motivated by the success of large-scale data-driven learning, we created RoboNet, an extensible and diverse dataset of robot interaction collected across four different research labs. The collaborative nature of this work allows us to easily capture diverse data in various lab settings across a wide variety of objects, robotic hardware, and camera viewpoints. Finally, we find that pre-training on RoboNet offers substantial performance gains compared to training from scratch in entirely new environments....

• Evaluating Classification Models, Part 3:
  Fᵦ and Other Weighted Pythagorean Means of Precision and Recall
  This post explains how the three weighted “Pythagorean means” (arithmetic, geometric, and harmonic) of precision and recall encode preferences over models...

• Inferring causality in time series data
  Inferring causality in time series data...

• Data Visualization with Python and JavaScript:
  Scrape, Clean, Explore & Transform Your Data< Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations...
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page
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  P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian